Most marketers are unsatisfied with the way their teams are localizing branded content for different markets, yet fail to prioritize this need in their budgets, a new CMO Council report revealed.

According to the report, 63% of marketers feel they’re “not doing well at all,” “need improvement,” or “getting better” when asked how effectively they adapt, modify and/or localize branded content for different markets, audiences, partners, and geographies. Just 33% rated themselves high, saying their organizations are “very advanced in this area” or “doing well.”

Despite the clear need to localize – with 50% of marketers saying it’s essential to business growth and profitability – most marketing teams simply do not have the budget to execute their goals. As high as 75% said they are spending 10% or less of their budgets on localization efforts.

Partnering with HH Global, the CMO Council released its “Age of Adaptive Marketer” report where it detailed the findings of a poll conducted among 150 marketing executives in a range of industries during the second quarter of 2017. The report included comments from the top management of US-headquartered companies Pepsi, Chobani, and Starwood Hotels and Resorts.

As consumers increasingly expect brands to engage with them in the most relevant ways, almost half of survey respondents cited localization demands – including language, cultural values, and other sensitivities – as the top factor “putting pressure” on marketing teams to more effectively deliver branded content at scale.

But at the same time, ensuring that content is properly localized (34%) without diluting the brand’s overall identity (43%), as well as shorter lead times and deadlines (47%) are among the biggest challenges for marketers.

“In today’s day and age, there is an expectation that customer experiences happen in total context to the consumer, yet localization – whether it’s around the globe or around the corner – is still a far-off goal for far too many organizations,” the CMO Council noted in its report.

With emojis increasingly showing up in everything from ad campaigns to legal cases, a clear understanding of what the symbols mean — especially across different cultures — has become an in-demand skill. So much so that last year, global firm Today Translations placed an ad for the position of “emoji translator.”

Following a months-long application process that included emoji tests and the drafting of an emoji handbook, the position was given to Irishman Keith Broni.

His qualifications encompass far more than frequent texting. Broni just completed his master’s in business psychology at University College London, where his dissertation was on “the influence that emojis can have in digital context when brands are using them to communicate with potential consumers.”

VICE News spoke to Broni about what exactly an emoji translator does, the problems that can arise when brands use emojis, and how to manage the ever-changing definitions of emojis and their varying uses across cultures.

Media content localization across Europe, Middle East and Africa (EMEA) is expected to increase from USD 2bn this year to USD 2.5bn before 2020, according to research conducted on behalf of the Media & Entertainment Services Alliance (MESA) Europe.

According to MESA, media content localization involves preparing TV, film and video titles ready for global distribution. Jim Bottoms, MESA Europe’s Executive Director, told Slator the market is covers subtitling, dubbing, video localization, and access services. Dubbing currently accounts for 70% of total spending, according to the MESA Europe report.

“There is a huge demand for content,” says Bottoms. “Some of it is new release, but a lot of it is is catalog or stuff that they thought would never sell again.”

Back catalog TV series and movie titles are finding new outlets and a new audience in regions where they haven’t been seen previously, as they are licensed by foreign channels to include in their programming to appeal to a particular demographic or age group.

“So, the program makers are suddenly finding that not only is there a huge demand for new release titles to go out to more and more markets. There is also a demand for getting some of their catalog product localized,” shares Bottoms.

MESA Europe noted that the strong growth in channels is also driven in part by so called over-the-top players OTT (i.e. content delivered over the Internet), which has opened up more opportunities for program makers to sell their titles into new markets.

Netflix, for one, ended the year 2016 with 93 million users, delivering about 150 million hours of streaming video per day. This was a year after the company announced the global rollout of its streaming service to 130 countries, which was previously available only in select countries. Amazon, meanwhile, made its Prime Video available in 200 countries in December 2016, competing head on with Netflix.

With the fast growing global demand for content, a shortage of talent has become one of the industry’s biggest challenges.

“Given the way the market is growing, there are already capacity shortages and this is likely to get worse in the short term,” explains Bottoms.

Of course, dubbing has been done for decades, but the current shortfall in talent is because of the massive growth as well as an indication that new talent isn’t coming through. As Bottoms points out, “In Germany in particular, the concern is that the talent is aging and perhaps younger people aren’t coming into the sector for whatever reason.”

From Publisher Perspectives: “Using his winnings from the International Dublin Award, translator Daniel Hahn has established his own new prize for emerging translators—and their equally overlooked editors.”

One good competition has led to another. On June 21, when author José Eduardo Agualusa’s A General Theory of Oblivionwas named winner of this year’s €100,000 (US$114,640) International Dublin Literary Award, the prize was split with translator Daniel Hahn.

The Dublin prize, now in operation for 22 years, is said to be the richest for a single novel published in English. When there’s a translator involved, the purse is divided, €75,000 going to the author and €25,000 to the translator. Having translated the book from the Portuguese, Hahn delivered Agualusa’s acceptance speech at Dublin’s Mansion House.

And then he took some of his own winnings and created a new award.

The TA First Translation Prize—”TA” for the UK’s Translators Association—is so new that it hasn’t yet been added to the Society of Authors list of other translation prizes the society administers. Antonia Lloyd-Jones, who is joint chair of the Translators Association, calls it “a ground-breaking addition to the world of literary translation. By encouraging talented new translators, as well as visionary editors, it will increase the range of great literature that’s available in translation, and strengthen the relationships between publishers and translators.”

Visionary editors? Yes, the prize’s £2,000 (US$2,570) purse will have something in common with the Dublin award, the Man Booker International Prize, and a few others. Just as those prizes are split between author and translator, the award Hahn has endowed will be split—equally, as the Booker does it—between a first-time translator and her or his editor.

In a conversation with Hahn from between Brighton and Lewes in Sussex, what comes across is that translators—at times overlooked and underappreciated in the industry–have learned the hard way how important it is to share recognition. And his prize honors new translators, Hahn says, because breaking into the business is so difficult without recognition.

“There’s a kind of bottleneck,” Hahn says. “If you’re a publisher and you want to commission a translation from Portuguese, you’ll ask Margaret [Jull Costa] and you’ll ask Alison [Entrekin], and if they can’t do it, you’ll ask one or two other people and,” he says wryly, “you might then ask me. But a new translator of Portuguese has relatively little odds of getting in because there’s a queue of people who are through the door already.”

And Hahn didn’t want to stop with his prize’s recognition of a new translator. “It’s funny,” he says, “we translators complain about not being sufficiently visible in our work—and I think it’s a legitimate complaint—but nobody thinks about editors. And not just for the acquisition and commissioning but for the actual editing. Something on behalf of our profession that recognizes that profession is important.”

Think about how many prizes you’ve encountered that honored editors. Right.

“If I’m a better translator than I was 10 years ago,” Hahn says, “it’s because I’ve been edited well.”

Trailers for season 7 of Game of Thrones have supporters of the various in-universe character factions on tenterhooks. Meanwhile, more dedicated and geeky fans of Game of Thrones might be able to appreciate a new option being offered by language translating app Duolingo: learning Valyrian.

Unlike English, High Valyrian uses an aorist tense, similar to Ancient Greek and Sanskrit. David J. Peterson, the linguist who created the Dothraki and Valyrian languages for the TV series, worked on the Duolingo course, so you can be assured any dragon-training commands you learn will be effective.

Peterson created the language mostly from scratch, constructing the grammar around the two key phrases used in George R.R. Martin’s A Song of Ice and Fire books: “Valar Morghulis” (“All men must die”) and “Valar Dohaeris” (“All men must serve”).

The contest was begun by a Mexico-based group, with each choosing a poem from the language they work in, presents it, and then solicits submissions. Rohana chose Mona Kareem’s poem “حدود.”

“Like many other poets she is also a translator,” Rohana said over email, “and and I chose ‘حدود‘ because it’s my favorite poem in her recent book.”

The contest open until end of July, and the Spanish translation of the poem should be sent directly to concurso1×1@gmail.com. There are no special requirements; the contest is open to all.

“The winner will be announced on the website,” Rohana said, “and there might be some ceremony involved in December—we’re still not so sure. This year we had concursos in Russian, Catalan and Mayan language. We are also trying to see what kind of prizes we can give, but for now what’s certain is that the winner will be published in places like Periódico de poesía, which is a poetry magazine that belongs to the UNAM (the National Autonomous University of Mexico) and another magazine called Sin Fin, and both are part of the organizers.”

The contest began two years ago as a translator-led initiative, and thus far it has featured poems from the German, Chinese, Italian, Japanese, Hebrew, Portuguese and modern Greek. Rohana added that, “Special emphasis is put on Mexican indigenous languages and there have been participations in Náhuatl, Ayuuk, Miixteco and Zapoteco. The idea is to celebrate literary dialogues, translation, poetry, poetry translation and linguistic diversity.”

The number of people employed in the translation and interpretation industry has doubled in the past seven years, and the number of companies in the industry has jumped 24 percent in that same time period, according to the ATA, citing data from the Department of Labor. Through 2024, the employment outlook for those in the business is projected to grow by 29 percent, according to the Bureau of Labor Statistics.

“As the economy becomes more globalized and businesses realize the need for translation and interpreting to market their products and services, the opportunities for people with advanced language skills will continue to grow sharply,” said David Rumsey, president of the ATA, adding that the association predicts the largest growth is within contracted positions, giving workers and companies more flexibility.

While salaries within the industry vary, those who specialize in a difficult language can easily bring in six figures annually. The ATA helps connect freelance translators and interpreters with companies including Microsoft, Netflix and Honda, as well as government agencies such as the State Department and FBI, Rumsey said.

Philadelphia-based CETRA Language Solutions and companies like it work with about 1,000 independent contractors in translation services in any given year and recruit on a daily basis. And while there was once a fear that technology would replace humans in the process as demand for services increased, the opposite has happened — it’s enhanced their work.

“The overall industry is growing because of the amount of content out there — it’s increasing exponentially,” said Jiri Stejskal, president and CEO of CETRA. “Technology is helping to translate more content, but for highly specialized content, you need an actual human involved.”

But finding successful employment is about much more than just speaking multiple languages fluently. Translators who want to distinguish themselves as professionals have to continue to work and hone their skill sets, the ATA’s Rumsey said.

“It’s a lifelong practice, and it requires keeping up not only your language skills but your subject matter skills so that you really understand the industries and fields you are working in,” Rumsey said.

For the fifth consecutive year Independent market research firm Common Sense Advisory recognizes Lionbridge as the world leader in the growing, $43 billion global language services industry

Waltham, Mass. – July 12, 2017 — Lionbridge Technologies, Inc., announced today its official ranking as the largest language services provider (LSP) in the global translation, localization and interpreting industry. Issued July 2017 by independent market research firm Common Sense Advisory (CSA Research), the report titled “The Language Services Market: 2017” ranked Lionbridge as a top-grossing LSP in the US $43.08 billion global market for outsourced language services and technology.

As part of the study, the firm surveyed providers from every continent to collect actual reported revenue for 2015, 2016 and expected revenue for 2017. Lionbridge leads the industry due to its innovative language technology-platform, its global program management excellence and its trusted network of in-country translation and localization professionals.

More than 800 global brands rely on Lionbridge to manage their business-critical content, applications and communications across channels, platforms and languages.

CSA Research, which has published market size estimates and global rankings for the past 13 years, found that the demand for language services and supporting technologies continues and is growing at an annual rate of 6.97%, representing an increase over last year’s rate of 5.52%.

Machine translation – the task of automatically translating between languages – is one of the most active research areas in the machine learning community. Among the many approaches to machine translation, sequence-to-sequence (“seq2seq”) models [1, 2] have recently enjoyed great success and have become the de facto standard in most commercial translation systems, such as Google Translate, thanks to its ability to use deep neural networks to capture sentence meanings. However, while there is an abundance of material on seq2seq models such as OpenNMT or tf-seq2seq, there is a lack of material that teaches people both the knowledge and the skills to easily build high-quality translation systems.

Today we are happy to announce a new Neural Machine Translation (NMT) tutorial for TensorFlowthat gives readers a full understanding of seq2seq models and shows how to build a competitive translation model from scratch. The tutorial is aimed at making the process as simple as possible, starting with some background knowledge on NMT and walking through code details to build a vanilla system. It then dives into the attention mechanism [3, 4], a key ingredient that allows NMT systems to handle long sentences. Finally, the tutorial provides details on how to replicate key features in the Google’s NMT (GNMT) system [5] to train on multiple GPUs.

The tutorial also contains detailed benchmark results, which users can replicate on their own. Our models provide a strong open-source baseline with performance on par with GNMT results [5]. We achieve 24.4 BLEU points on the popular WMT’14 English-German translation task.
Other benchmark results (English-Vietnamese, German-English) can be found in the tutorial.

In addition, this tutorial showcases the fully dynamic seq2seq API (released with TensorFlow 1.2) aimed at making building seq2seq models clean and easy:

Easily read and preprocess dynamically sized input sequences using the new input pipeline in tf.contrib.data.

Use padded batching and sequence length bucketing to improve training and inference speeds.

Train seq2seq models using popular architectures and training schedules, including several types of attention and scheduled sampling.

Perform inference in seq2seq models using in-graph beam search.

Optimize seq2seq models for multi-GPU settings.

We hope this will help spur the creation of, and experimentation with, many new NMT models by the research community. To get started on your own research, check out the tutorial on GitHub!

Today’s post is about the improvements in the field of terminology support for interpreters through computer-assisted interpreting (CAI) tools. InterpretBank is an example of such tools, it was developed as part of a PhD project and it uses IATE as one of its terminology sources. Our guest writer Claudio Fantinuoli (Johannes Gutenberg University Mainz in Germersheim) tells us all about it.

InterpretBank is a computer-assisted interpreting (CAI) tool originally developed at the Johannes Gutenberg Universität Mainz in Germersheim as part of a PhD research project. The objective of this project was to create a computer program to support professional interpreters during all phases of the interpreting workflow, from preparation to the act of interpreting. With the aim of improving interpreting quality especially in the context of specialised events, InterpretBank focuses on the creation and management of specialised glossaries as well as on facilitating terminology memorization and retrieval during interpretation.

InterpretBank implements the results of several years of research and the feedbacks of a growing number of users. The tool integrates automatic translation and high-quality terminology databases, such as IATE, to reduce the effort and the time involved in writing glossaries. During preparation, a memorization utility helps interpreters learning the event-related terms. While interpreting, intelligent algorithms allow the user to access relevant terminology quickly and without distracting the interpreter from his or her primary activity – translating between languages. Several independent studies have confirmed that the tool can contribute to increasing the overall interpreting quality. We have now taken a further step forward integrating Speech Recognition.

The interest for the emerging field of CAI tools is growing: InterpretBank is taught in a large number of universities and in dedicated seminars held by professional associations around the world. InterpretBank is the tool of choice not only of many professionals but also when it comes to empirical research in the field of translation technology. In Germersheim, for example, an ongoing PhD project is investigating cognitive load in simultaneous interpreting with the support of terminology management tools.

Speech-to-Speech (S2S) technology seems to have finally stepped out of the realm of science fiction, yet it’s not ready for prime time. In their report published earlier this year, the Translation Automation User Society (TAUS) recognizes this as the paradox the technology currently finds itself in.

The report outlines the current status, future directions, challenges, and opportunities of speech translation. It also includes interviews with 13 people who represent institutes and companies researching and working in this field. We present highlights from the report.

New directions and possibilities

Ike Sagie of Lexifone believes that existing engines for Machine Translation (MT) and Speech Recognition (SR) cannot be used straightaway. Optimization layers and other modifications are also required. Since people speak continuously, there must be an acoustic solution that cuts the flow into sentences or segments and sends the output to an audio optimization layer. Linguistic optimization is needed in the next stage to ensure translation accuracy, such as making sure interrogative sentences are annotated with question marks.

Chris Wendt of Microsoft/Skype states that SR, MT, and Text-to-Speech (TTS) by themselves are not enough to make a translated conversation work. Because clean input is necessary for translation, elements of spontaneous language—hesitations, repetitions, corrections, etc.—must be cleaned between automatic SR and MT. For this purpose, Microsoft has built a function called TrueText to turn what you said into what you wanted to say. Because it’s trained on real-world data, it works best on the most common mistakes, Wendt says.

According to Chengqing Zong from the Chinese Academy of Sciences, future advancements in S2S technology may also include different means of evaluating quality than current automatic techniques such as Bleu Scores. In the future, Zong says, “We’ll rely more on human judgment. Work on neural networks will continue, despite problems with speed and data sparseness.”

Computer-assisted translation (CAT) tools

OmegaT CAT tool. Here you see the translation memory (Fuzzy Matches) and terminology recall (Glossary) features at work. OmegaT is licensed under the GNU Public License version 3+.

CAT tools are a staple of the language services industry. As the name implies, CAT tools help translators perform the tasks of translation, bilingual review, and monolingual review as quickly as possible and with the highest possible consistency through reuse of translated content (also known as translation memory). Translation memory and terminology recall are two central features of CAT tools. They enable a translator to reuse previously translated content from old projects in new projects. This allows them to translate a high volume of words in a shorter amount of time while maintaining a high level of quality through terminology and style consistency. This is especially handy for localization, as text in a lot of software and web UIs is often the same across platforms and applications. CAT tools are standalone pieces of software though, requiring translators that use them to work locally and merge to a central repository.

Machine translation (MT) engines

MT engines automate the transfer of text from one language to another. MT is broken up into three primary methodologies: rules-based, statistical, and neural (which is the new player). The most widespread MT methodology is statistical, which (in very brief terms) draws conclusions about the interconnectedness of a pair of languages by running statistical analyses over annotated bilingual corpus data using n-gram models. When a new source language phrase is introduced to the engine for translation, it looks within its analyzed corpus data to find statistically relevant equivalents, which it produces in the target language. MT can be useful as a productivity aid to translators, changing their primary task from translating a source text to a target text to post-editing the MT engine’s target language output. I don’t recommend using raw MT output in localizations, but if your community is trained in the art of post-editing, MT can be a useful tool to help them make large volumes of contributions.

Translation management systems (TMS)

Mozilla’s Pontoon translation management system user interface. With WYSIWYG editing, you can translate content in context and simultaneously perform translation and quality assurance. Pontoon is licensed under the BSD 3-clause New or Revised License.

TMS tools are web-based platforms that allow you to manage a localization project and enable translators and reviewers to do what they do best. Most TMS tools aim to automate many manual parts of the localization process by including version control system (VCS) integrations, cloud services integrations, project reporting, as well as the standard translation memory and terminology recall features. These tools are most amenable to community localization or translation projects, as they allow large groups of translators and reviewers to contribute to a project. Some also use a WYSIWYG editor to give translators context for their translations. This added context improves translation accuracy and cuts down on the amount of time a translator has to wait between doing the translation and reviewing the translation within the user interface.

Terminology management tools

Brigham Young University’s BaseTerm tool displays the new-term entry dialogue window. BaseTerm is licensed under the Eclipse Public License.

Terminology management tools give you a GUI to create terminology resources (known as termbases) to add context and ensure translation consistency. These resources are consumed by CAT tools and TMS platforms to aid translators in the process of translation. For languages in which a term could be either a noun or a verb based on the context, terminology management tools allows you to add metadata for a term that labels its gender, part of speech, monolingual definition, as well as context clues. Terminology management is often an underserved, but no less important, part of the localization process. In both the open source and proprietary ecosystems, there are only a small handful of options available.

Localization automation tools

The Ratel and Rainbow components of the Okapi Framework. Photo courtesy of the Okapi Framework. The Okapi Framework is licensed under the Apache License version 2.0.

Localization automation tools facilitate the way you process localization data. This can include text extraction, file format conversion, tokenization, VCS synchronization, term extraction, pre-translation, and various quality checks over common localization standard file formats. In some tool suites, like the Okapi Framework, you can create automation pipelines for performing various localization tasks. This can be very useful for a variety of situations, but their main utility is in the time they save by automating many tasks. They can also move you closer to a more continuous localization process.

BabelOn, a startup in the San Fransisco area, is developing software that can transform your speech from English to any other language, without using any additional translation services, and it will sound like you own voice. While using artificial means to create the sounds of a human voice, a technique called speech synthesis, has been around for a while, BabelOn is offering a very specific and unique spin on the technology. Using a specialized combination of custom designed hardware and software, BabelOn will analyze your voice for its unique characteristics then use those results to recreate language that sounds like the words are coming out of your own mouth in any language you want.

Originally the idea was conceived for use in film dubbing or translating video games but the ultimate goal for BabelOn is to provide real-time language translation in your voice, like when you’re on a Skype call or similar circumstances. Although Microsoft currently offers a comparable service for some time, their voice is digital sounding, like Siri, making the BabelOn difference more personal.

While this is certainly a very interesting concept, it is still very early on in BabelOn’s development. There has yet to be a software demonstration, nor have they done any work for clients. Currently, BabelOn is bidding for a soon to be released video game translation but it is not a done deal. The software has a potential for success but also presents a glaring security concern in the concept of having one’s voice “stolen”.

Issues related to gender, the workplace, and family are important social and political concerns. “Gender and Family in the Language Services Industry” is the first in a series of reports by independent market research firm Common Sense Advisory (CSA Research) dealing with gender and family issues among those who are employed in the language industry – translation, localization, interpreting, and related tasks – or who work with language services.

What matters most to you in your career?

Earnings ReportsEconomic studies show that jobs considered “women’s work” typically show lower pay than those associated with men, and that wages fall as more women enter a field. By contrast, both genders earn above-average wages in language services: US$50,900 in North America and $34,800 in Europe (versus economy-wide averages of US$49,630 and roughly US$20,000, respectively).

“These results are generally positive. Despite significant downward price pressures, language services professionals’ earnings are in line with – or exceed – those of other skilled professionals, without the penalty often associated with ‘women’s work,’” comments Arle Lommel, a senior analyst at CSA Research.

Key Findings and DatasetBased on 2,200 global responses, the CSA Research report shines light on topics ranging from pay to personality to promotions. Key findings from the report, which is available for free with registration, include:

The world-wide language services gender pay gap is 19%. Adjusting for employment status, men still make 14% more than women. Earning disparities are highest in top and middle management and executive positions.

The gender gap is lowest in North America, but higher in Europe. However, Europeans believe they are closer to pay equality than others and Americans believe their employers are less equitable than others.

Majorities believe that gender issues do not affect them personally. Women are more likely to believe they have been personally affected, but both men and women, tend to see gender issues as important problems that affect other people.

Both men and women see women as having more positive qualities as employees. Respondents of both genders tend to agree that women have more positive qualities – including those important for leadership roles – than men do. Nevertheless, these qualities do not convert into advancement opportunities.

In June 2017, a German federal court published a ruling in a tax case that delved into the details of where exactly freelancing ends (German: freiberufliche Tätigkeit) and a commercial enterprise begins (German: gewerbliche Tätigkeit).

As Slator reported, the court ruled that freelance translators are not allowed to offer languages they don’t personally understand and continue to enjoy the tax breaks and other administrative benefits that come from operating in a freelance capacity.

On Twitter and LinkedIn, the Slator article triggered an interesting debate about outsourcing to fellow freelancers and Slator’s take of the ruling (we stand by it). Beyond the ruling in Germany, subcontracting by freelancers has been a hot-button issue for decades, and so we wanted to know how our readers perceive the practice.

A clear majority of the 118 respondents who participated in the poll conducted among Slator’s e-mail newsletter subscribers view subcontracting unfavorably — 22% of respondents say it depends and only 10% approve of subcontracting.

If there is an industry that could benefit from more efficient payment technology, it’s the language services industry. Global in nature, hundreds of thousands of freelancers are based in every possible country under the sun, and millions upon millions of relatively small payments are made every single month.

But despite a boom in fintech, two decades of PayPal, and cryptocurrencies like Bitcoin becoming more mainstream, bank transfers, which often involve expensive fees, remain the top choice of paying freelancers among the respondents to our poll. Expect this to change in the coming decade.

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